The present invention relates generally to image segmentation, or the process of assigning labels to the elements in an image. More specifically, it relates to semi-automatic 3D medical image segmentation, where there is an interactive process for labeling each voxel according to tissue type represented.
Image segmentation has been an active area of research in the computer vision community, and the medical image analysis community. The ultimate goal is often a fully automatic algorithm that need not require input from a human user. In practical applications, however, stringent goals for accuracy may compel assistance from an expert. Semi-automatic algorithms may query the user for seed points to initiate region growing, or training points to initialize probability distributions (used by Bayesian or kNN classification), or threshold levels to govern expansion terms of level set methods, or strokes of a virtual paint brush to indicate foreground and background objects (used by level set or GrowCut algorithms), or bounding boxes to encapsulate regions of interest to provide spatial constraints. Furthermore, they may allow interactive edits of automatically-computed results by relocating control points of active contours, or by manipulating tools for repositioning contours. It may behoove the user to manually redraw an incorrect border astride the perimeter of a structure. Fully manual segmentation involves a person drawing all the boundaries of all structures, but such tedious monotony is prone to error and inter-observer variability. Any method less than nearly fully automatic could be prohibitively expensive to deploy in clinical settings due to how much time is consumed by healthcare personnel. The user interface device is usually a computer mouse, stylus, or touch screen, but could be a trackball, haptic interface, or eye-gaze detector in academic settings.
Segmented images are essential for various clinical applications that stand to benefit from the presence of images where each relevant anatomic structure has been delineated. Segmentation can be a valuable ally in treating cancer, whether by radiotherapy, chemotherapy or surgical resection. Image guided radiation therapy (IGRT) uses cross-sectional images of the patient's internal anatomy to better target the radiation dose to the tumor while sparing exposure of healthy organs. The radiation dose delivered is controlled with intensity modulated radiation therapy (IMRT), which involves changing the size, shape, and intensity of the radiation beam to conform to the size, shape, and location of the patient's tumor. IGRT and IMRT simultaneously improve control of the tumor while reducing the potential for acute side effects due to irradiation of healthy tissue surrounding the tumor. Segmentation is widely employed for IGRT and IMRT because the process of planning the delivery is a quantitative and numerical exercise best suited for a computer. Chemotherapy, in contrast to radiotherapy, tends to follow a more qualitative planning process whereby the tumor's response to the treatment regimen is visually monitored, such as by a CT scan every couple months. Precise quantification of tumor extent would be useful for decision making, but oncologists are too short on time to be guiding semi-automatic segmentation methods, and they're unlikely to be trained in using expensive analysis workstations. Surgical resections and biopsies benefit from image segmentation by rendering 3D views of the spatial relationships between organs for surgical planning and guidance. Beyond treating cancer, image segmentation is utilized in longitudinal studies that track quantitative measurements such as anatomic dimensions, cross-sectional areas, or volumes.
Recent improvements in speed, accuracy, and automation of segmentation algorithms have nearly obviated human intervention in certain research applications. These applications tend to focus on tissue that appears normal, such as quantitative measurements of neuroanatomy. Disease can vary in unexpected ways that are complicated to model, and disease often presents special cases and outliers that extend beyond the understanding of computer software. What the software needs is interaction with a keen physician, quick and clever, to astutely manipulate facts.
Even if fully automatic algorithms could become sufficiently accurate for routine clinical use, certain physicians vary in personal approach and requirements, so algorithms would still benefit from some manner of catering to individual preferences. When the full knowledge and artful discernment of the physician(s) is reflected in the output of the segmentation, then the downstream processes to which segmentation is an input can become effectual instruments.
The foregoing discussion highlights the need for new semi-automatic strategies that can incorporate the expertise of the physician(s) into the segmentation process. The key enabler is to employ their penetrating intellect with a minimum of time and expense. The present invention proposes voice-activation as this key enabler. Voice recognition has a history of employment by the medical profession for dictation and medical transcription. Healthcare researchers have also experimented with voice-activated image retrieval, operating an imaging scanner by voice commands, and hands-free manipulation of a display of 3-D angiography by a surgeon in the operating theater.